



"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class       snake_case_    (    _lowerCAmelCase					):
							__lowerCAmelCase            =       ["image_processor", "tokenizer"]
							__lowerCAmelCase            =       "ViTImageProcessor"
							__lowerCAmelCase            =       ("CLIPTokenizer", "CLIPTokenizerFast")






							def __init__( self      ,  a_=None      ,  a_=None      ,  **a_   ):
									a_  :   Union[str, Any]			    =  None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."      ,  SCREAMING_SNAKE_CASE_      ,  )
											a_  :   str			    =  kwargs.pop("feature_extractor"   )

									a_  :   int			    =  image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."   )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."   )

									super().__init__(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_   )






							def __call__( self      ,  a_=None      ,  a_=None      ,  a_=None      ,  a_=None      ,  **a_   ):
									if text is None and visual_prompt is None and images is None:
											raise ValueError("You have to specify either text, visual prompt or images."   )

									if text is not None and visual_prompt is not None:
											raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

									if text is not None:
											a_  :   int			    =  self.tokenizer(SCREAMING_SNAKE_CASE_      ,  return_tensors=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )

									if visual_prompt is not None:
											a_  :   int			    =  self.image_processor(SCREAMING_SNAKE_CASE_      ,  return_tensors=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )

									if images is not None:
											a_  :   List[Any]			    =  self.image_processor(SCREAMING_SNAKE_CASE_      ,  return_tensors=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )

									if visual_prompt is not None and images is not None:
											a_  :   Tuple			    =  {
											    """pixel_values""": image_features.pixel_values,
											    """conditional_pixel_values""": prompt_features.pixel_values,
											}
											return encoding
									elif text is not None and images is not None:
											a_  :   Tuple			    =  image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									elif visual_prompt is not None:
											a_  :   List[Any]			    =  {
											    """conditional_pixel_values""": prompt_features.pixel_values,
											}
											return encoding
									else:
											return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_   )      ,  tensor_type=SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self      ,  *a_      ,  **a_   ):
									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self      ,  *a_      ,  **a_   ):
									return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )






							@property
							def snake_case_	( self   ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,  SCREAMING_SNAKE_CASE_      ,  )
									return self.image_processor_class






							@property
							def snake_case_	( self   ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,  SCREAMING_SNAKE_CASE_      ,  )
									return self.image_processor




import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






